It’s our belief that effective platforms are key to delivering ML and AI at scale. These platforms support data science and ML engineering teams by allowing them to innovate more quickly and consistently. So what is a machine learning platform? A machine learning platform is a set of tools and technologies (backed by a set of practices and processes) established by an organization to support and automate various aspects of the machine learning workflow, including data acquisition, feature and experiment management, and model development, deployment, and monitoring.
Machine learning platforms come in a wide variety of forms. Until recently, they have primarily been found at large technology companies, which have developed their platforms internally, out of necessity, to support increasingly significant investments in machine learning. As the importance of machine learning has become clear to a broader array of enterprises, new commercial and open source ML platform technologies have become available to reduce the barriers to adoption and make the benefits of ML models more accessible.
Some of the benefits of an end to end platform are really also the benefits of MLOps generally, namely:
By abstracting across the experiences of many early platform builders and users, and identifying the capabilities that we see frequently recurring in the platforms that they’ve built, we have identified a core set of platform capabilities.
It’s important to note that these End-to-End ML Platforms encompass three other sub-categories:
In addition to the above, they generally include system-wide functions such as: